Testing an optical characteristic of a camera component

ABSTRACT

A camera module under test is signaled to capture an image of a target. A group of pixels of the image that represent portions of several objects in the target are low pass filtered and then analyzed to compute a pixel distance between different subgroups of pixels that represent portions of the different objects. The computed pixel distance is then converted into a true distance using a predetermined math relationship that relates a pixel distance variable with a true distance variable.

This disclosure relates to a method for testing an opticalcharacteristic of a camera component, using image processing techniques.Other aspects are also described.

BACKGROUND

The ability of a camera to produce a photograph that faithfully depictsa scene is governed in large part by its optical performance. Withrespect to modern consumer electronic devices, such as portablecomputers (laptops, tablet computers, and smart phones), as well as indesktop computers, improvements in image quality have been achievedtogether with the use of higher megapixel image sensors that are beingincorporated into relatively small or tightly confined spaces within thehousing of the consumer electronics device. As the dimensions of theoptical components shrink, including, for instance, the imaging lensitself and its distance to an image sensor at the focal plane, thephotograph produced by the camera becomes more susceptible to slightdeviations in position and alignment of the optical system components.For instance, slight changes in the alignment of a lens relative to theoptical axis and relative to the image sensor can result in imagequality degradation. Such misalignments can occur during manufacturingand, in particular, during component assembly of a camera module inwhich the lens may be integrated.

There are several technical variables that can be used to evaluate theoptical performance of a camera based on the quality of the resultingphotograph of picture. For instance, there are image processingtechniques to measure the sharpness of a photograph. Sharpness is alsotypically monitored during an auto-focus process in which a sharpnessvalue is calculated over a number of different image captures or frames,as the distance between a camera lens and a plane of the image sensor ischanged. The auto-focus process, of course, attempts to find the optimumlocation of a moveable lens along the z-axis (optical axis) that yieldsthe sharpest captured image. Another optical characteristic that isoften evaluated is distortion, that is whether a geometric shape of theobject has been distorted (e.g., where a straight line appears slightlycurved).

There are measurements of optical characteristics that are performed ona camera component, such as a lens, during manufacture testing, toensure that the specimens released to end users are within a givenperformance specification. One characteristic that is tested is that ofoptical tilt. The camera component is installed in a test fixture whilealigned with a target test pattern, and high precision mechanicalmeasurement components including a laser light source and mirrors areused together with the needed automatic test equipment to mechanicallymeasure the tilt of the camera component.

SUMMARY

There are times when it is desirable to know whether or not a lens hasbeen inadvertently moved relative to a test fixture or relative to acamera component housing in which it has been installed. Thisinformation is useful when testing the optical characteristics of thecamera component, particularly during manufacture testing, to inform thedecision as to whether the lens itself has a defect or whether theresulting subpar imaging performance is due to an unintentional shift inthe position of the lens relative to the image sensor. For instance,when testing a camera module that will be installed into a consumerelectronic device, such as a smart phone or a tablet computer, a lensassembly may be installed in the module prior to a verification test ofthe optical or imaging performance of the module. Because of therelatively small dimensions of such a camera module, small shifts orchanges in the position of the lens will impact the results of a testwhich evaluates, for instance, the sharpness performance of the cameramodule. In particular, during manufacturing of an autofocus cameramodule, it may be that the test fixture used for checking the autofocusfunctionality inadvertently injects inaccuracies in the form of small,undesired changes in the distance between an autofocus lens and theimage sensor. Such events may be more of a concern for high volumemanufacture specimens of the camera module which need both low cost andefficient testing procedures. It has been found that a technique isneeded to determine when such an event has occurred, and how much thelens distance has changed. It should be noted that a dedicated precisiondistance measurement tool (that could easily measure the distancebetween the lens and the image sensor) is not practical at this assemblystage of manufacture since the camera module is quite compact in thecase of certain consumer electronic devices (e.g., smart phones andtablet computer), and as such cannot be easily fitted with such amechanical measurement tool.

In accordance with an embodiment of the invention, a method for testingan optical characteristic of a camera component proceeds as follows. Animaging target having a number of objects (e.g., an edge pattern)therein is brought into the field of view of a camera component, such asa consumer electronic camera module. A digital image of the objects thatappear in the target is then captured (based on an optical image of theobjects that has been formed through the camera component). A group ofpixels of the digital image that represent portions of the objects islow pass filtered. The filtered group of pixels is then analyzed tocompute a pixel distance between a first subgroup of pixels thatrepresents a portion of one of the objects, and a second subgroup ofpixels that represents a portion of another one of the objects. Thepixel distance is then converted into a true distance (e.g., in units ofmicrons), using a predetermined math relationship, where the latterrelates a pixel distance variable with a true distance variable. Thetrue distance variable gives the distance, e.g. in units of microns,between a lens of the camera component and a digital image sensor thatis used to capture the image.

In one embodiment, a baseline or initial distance is computed just aftera camera component (device under test) has been installed in a testfixture. Some time later, a new distance is computed (using the samecamera component), for example during an on-going manufacture testprocess for the component. An alert may then be signaled if thedifference between the baseline and new distance values is greater thana predetermined threshold. The alert may be used to inform a subsequentdecision on whether or not the camera component that is currentlyinstalled in the test fixture has shifted sufficiently so that animaging performance test should be repeated, with the component at itscurrent position.

To further improve accuracy, during the analysis of the captured image,the filtered group of pixels may be analyzed by calculating a centroidof the first subgroup, and a centroid of the second subgroup of pixels,where the computed pixel distance is taken as the distance between thecentroids.

The above summary does not include an exhaustive list of all aspects ofthe present invention. It is contemplated that the invention includesall systems and methods that can be practiced from all suitablecombinations of the various aspects summarized above, as well as thosedisclosed in the Detailed Description below and particularly pointed outin the claims filed with the application. Such combinations haveparticular advantages not specifically recited in the above summary.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example andnot by way of limitation in the figures of the accompanying drawings inwhich like references indicate similar elements. It should be noted thatreferences to “an” or “one” embodiment of the invention in thisdisclosure are not necessarily to the same embodiment, and they mean atleast one.

FIG. 1 is a block diagram of a system for testing an opticalcharacteristic of a camera component.

FIG. 2 depicts details of the pixel processing block that may be used tocompute the z-distance of a camera component.

FIG. 3 shows plots of pixel intensity versus pixel position that havebeen captured by the image sensor for an example test target.

FIG. 4 depicts the general form of an example math relationship thatrelates a pixel distance variable (y-axis) with a lens position or truedistance variable (x-axis).

FIG. 5 is a block diagram of a system for obtaining a best fit curve asan example of the math relationship between computed pixel distancevariable and a z-distance variable.

DETAILED DESCRIPTION

Several embodiments of the invention with reference to the appendeddrawings are now explained. Whenever the shapes, relative positions andother aspects of the parts described in the embodiments are not clearlydefined, the scope of the invention is not limited only to the partsshown, which are meant merely for the purpose of illustration. Also,while numerous details are set forth, it is understood that someembodiments of the invention may be practiced without these details. Inother instances, well-known circuits, structures, and techniques havenot been shown in detail so as not to obscure the understanding of thisdescription.

FIG. 1 is a block diagram of a system for testing an opticalcharacteristic of a camera component. The camera component in this caseis a lens 2, which is an imaging lens that has been fixed in a lensholder. The lens holder and the lens 2 may be installed in a testfixture 6 in which an image sensor 5 has also been installed. In thisarrangement, the image sensor 5 is also referred to as a focal planeimaging sensor because it is at a focal plane of an optical system thatincludes the lens 2 and that can capture a picture (still or video) of ascene which contains, in this case, a fixed target 1. The lens 2 and theimage sensor 5 may be in different pieces that have been combined into asingle assembly referred to as a camera module. The camera module may beone that is typically found in multi-function consumer electronicdevices that have camera functions, such as smart phones, tabletcomputers, desktop computers, and home entertainment systems. There mayalso be other optical components that are integrated with the lens 2 andthe image sensor 5 within a frame forming the single assembly of thecamera module. Examples include optical filters and apertures.

The camera module may be a device under test (DUT), and in particular ahigh volume manufacture specimen; in that case, the test fixture 6 is ahardware arrangement in a manufacturing setting. As an alternative, thetest fixture 6 may be in a development laboratory setting, or even in apost manufacturing product return and testing center. The test fixture 6should be designed to precisely fix and align the DUT relative to thefixed target 1, enabling the image sensor 5 to be used to capture imagesof the target 1 for evaluating the optical performance of the DUT.

The fixed target 1 has several objects including object 3 and object 4,for instance, and is located such that the camera component DUT asinstalled is aimed at the target as shown. The target 1 may be at apredetermined distance from the installed camera component. Each object3, 4 may be any dark-light contrasting region, e.g. an edge, which willbe captured by the image sensor 5. The target 1 may have differentobjects that are suitable for testing the optical performance of animaging system, but in this example the objects 3, 4 are oppositeportions of a rectangular light frame on a dark background. An image ofat least a part of this frame is to be captured by the image sensor 5,and an example pixel processing region as shown in FIG. 1 is selected inthe captured image, to be evaluated by a tester computer.

The tester computer may include the following elements. Note that thetester computer may be any suitable computing or data processing system(which includes, typically, a processor and memory combination) that isprogrammed to signal the image sensor 5 to capture an image of thetarget 1 and particularly the objects 3 and 4 therein, and then feedsthe image data to its pixel processing unit 7. The latter performsvarious math operations to estimate or compute a measure of az-distance, which is indicated in the example of FIG. 1 as the distancebetween the lens 2 and the focal plane image sensor 5 along thehorizontal center optical axis of the camera component. The testcomputer may also include an alert signaling unit 10 that evaluates thecomputed z-distance, by for instance comparing it with an expectedz-distance or other comparison threshold; if the comparison indicatesthat the computed z-distance is sufficiently different than a thresholdthan an alert is signaled or an error log is updated with an indicationthat the current camera component under test may be non-conforming ormay have been shifted from its initial positioning.

FIG. 2 gives additional details of the pixel processing unit 7. One ofthe tasks of the pixel processing unit 7 is to compute the pixeldistance between the two objects 3, 4 that are depicted in a givencaptured image or frame, and to convert the calculation from pixel spaceto true distance. Accordingly, a pixel distance calculator 8 is shownthat receives one or more captured images or frames from the imagesensor 5, and analyzes the pixels in the frame to compute a pixeldistance between its detected object 3 and object 4. The pixel distancecalculator may also include a detection operation, where an incomingimage is analyzed to first detect the objects 3, 4, that is identify agroup of pixels representing each object or edge.

Having identified the group of pixels in which the objects 3, 4 arecontained, these pixels are then low pass filtered or smoothed, beforeperforming an analysis that computes a pixel distance value y_(i). FIG.3 shows an example graph of pixel intensity versus pixel position for agroup of pixels, in two different frames taken from the same target 1.The process described here may thus be performed on more than one frame,and some form of averaging or statistical combination may then beperformed in order to obtain a single value of the pixel distance y_(i).The graph shown in FIG. 3 reflects the objects depicted in FIG. 1following low pass filtering. The pixel intensity begins, at the left,with relatively low values, and then increases to high values(corresponding to a light region) and then drops back down to the lowvalues (corresponding to the dark region) within the frame, and thenrepeats for the second object. The pixel distance calculator 8 performsan analysis upon the filtered group of pixels (based on the indicatedpixel processing region) in order to compute the pixel distance y_(i)that is between a first subgroup of pixels that represents a portion ofobject 3 and a second subgroup of pixels that represents a portion ofobject 4. Any suitable pixel processing algorithm may be selected thatcan compute the pixel location of an approximate peak intensity valuefor each pixel subgroup. However, it has been found that the analysisshould first include the calculation of a centroid of each subgroup,such that the pixel distance y_(i) is computed as being the distancebetween or separating the two centroids. Such a calculation for thepixel distance y_(i) may be repeated for more than one frame and perhapsthen averaged or otherwise statistically combined to form a singlevalue.

Returning briefly to FIG. 1, it can be seen that a pixel processingregion which contains the group of pixels to be analyzed may be definedby a selected number of pixel rows of the captured image that areexpected to contain the detected objects 3, 4. Here, the term “row” isbeing used generically and for convenience, to alternatively refer to acolumn of pixels. The low pass filtering may produce a single row ofpixel values (see FIG. 3 for instance) that are expected to containportions of the detected objects 3, 4. While row processing may enablemore efficient mathematical processing of pixel intensity and pixelposition values, the pixel processing region may be defined differently,e.g. along a diagonal for instance.

Returning to FIG. 2, a further element of the pixel processing unit 7 isa conversion unit 9 which serves to convert a computed pixel distancey_(i) into a true z-distance value, using a predetermined mathrelationship that relates a pixel distance variable (pixel coordinatespace) with a true distance variable (distance coordinate space, e.g.microns). The math relationship may have been predetermined bycollecting several direct physical measurements of true distance(between, for example, the lens 2 of a specimen of a camera componentdesign that is to be tested, and the image sensor 5), together withcorresponding computed/estimate pixel distance values, and thenanalyzing the collected data to find a best-fit curve. The results ofsuch a process are depicted in FIG. 4 by an example graph of estimatepixel distance y_(i) values versus measured z-distance. The resultingbest-fit curve in this instance is a two-dimensional linear curve,namely a line, which can be described by a linear equation. Of course, anon-linear curve or a curve containing non-linear sections may also beused. The best-fit curve is then stored in the tester computer (seeFIG. 1) and then reused when testing other specimens of the same cameracomponent design or the same camera component specification, forconverting from a calculated pixel space value to a true distance value.As suggested above, this true distance may be an accurate estimate ofthe distance along the optical axis between the lens 2 and the focalplane image sensor 5 (see FIG. 1).

FIG. 5 is a block diagram of a system for obtaining the best-fit curve,which is the math relationship that is needed to relate a computed pixeldistance variable and a true z-distance variable. The setup in FIG. 5may be similar to that of FIG. 1 in that a camera component having thesame design or specification as the device under test (including thesame design or spec for the lens 2 and the image sensor 5) is installedwithin a test fixture, and is aimed at the fixed target 1. Thisarrangement may be the same as that which will be subsequently used, inaccordance with FIG. 1, for performing high volume manufacture testingof the actual production specimens of the camera component design. Thesystem in FIG. 5 is also fitted with a mechanical measurement tool 12which is able to precisely and mechanically measure the z-distance, inthis case the distance between the lens 2 and the image sensor 5. Themechanical measurement tool 12 may be in accordance with a conventionallaser-based precision distance measurement technique. In addition, theholder and its lens 2 are allowed to be moveable in the test fixture,under control of an actuator 11. The latter may be a manual actuatorsuch as a precision screw drive mechanism that allows extremely smallmovements of the lens holder. Such movements are on the order of thoseexpected to appear due to an unintentional disturbance to the testfixture and/or an installed DUT (e.g., during high volume manufacturetesting). The image sensor 5 is coupled to provide its output data to apixel distance calculator 8, similar to the one used in the pixelprocessing unit 7 of FIG. 1. Each pair of computed or estimated pixeldistance value y_(i) and measured z-distance value z_(i) is then fed asa data point, to a best-fit curve finder 13. Several such data pointsare obtained (as the actuator 11 moves the lens holder to slightlydifferent positions, or as the lens is “swept” along the optical axis).Once a sufficient number of data points have been collected, the curvefinder 13 may compute the best-fit curve, e.g. using conventional curvefitting techniques. In other words, based on a collection of theexperimental data points, generated by the mechanical measurement tool12 and the pixel distance calculator 8, a curve is fit to the datapoints, e.g. in the manner illustrated by the graph of FIG. 4. Theresulting best fit curve (mathematical relationship) is then stored tobe reused during high volume manufacture testing of production specimensof the camera component, without the need for the mechanical measurementtool 12 and without the presence of the actuator 11 (in other words, anarrangement similar to the arrangement in FIG. 1).

As explained above, an embodiment of the invention may be amachine-readable medium (such as microelectronic memory) having storedthereon instructions, which program one or more data processingcomponents (generically referred to here as a “processor”) to performthe digital pixel processing operations described above including lowpass filtering or image smoothing, centroid calculation, and other mathfunctions. In other embodiments, some of these operations might beperformed by specific hardware components that contain hardwired logic(e.g., dedicated digital filter blocks). Those operations mightalternatively be performed by any combination of programmed dataprocessing components and fixed hardwired circuit components.

While certain embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of and not restrictive on the broad invention, andthat the invention is not limited to the specific constructions andarrangements shown and described, since various other modifications mayoccur to those of ordinary skill in the art. For example, although thefixed target depicted in FIG. 1 is a rectangular light frame on a darkbackground, other targets and their constituent objects (havingdifferent shapes) are possible. Also, while z-distance is depicted inFIG. 1 and in FIG. 5 as the distance between the image sensor (at thefocal plane) and a lens, an alternative definition may be the distancebetween the lens and the fixed target, or between the lens and someother fixed point in the system that can be mechanically measured(preferably along the optical axis as shown). While other z-distancedefinitions are possible (e.g. a distance that is not along the opticalaxis as shown in the figures), it is expected that a z-distance asmeasured along the optical axis may be the most practical way todetermine whether or not the lens has shifted. The description is thusto be regarded as illustrative instead of limiting.

What is claimed is:
 1. A method for testing an optical characteristic ofa camera component, comprising: a) capturing a digital image of aplurality of objects that appear in an imaging target, while an opticalimage of the objects is formed through the camera component; b) low passfiltering a group of pixels of the digital image that represent portionsof the objects; c) analyzing the filtered group of pixels to compute apixel distance between a first subgroup of pixels that represents aportion of one of the objects, and a second subgroup of pixels thatrepresents a portion of another of the objects; and d) converting thepixel distance into a true distance using a predetermined mathrelationship that relates a pixel distance variable with a true distancevariable.
 2. The method of claim 1 wherein the true distance variablegives the distance between a lens of the camera component and a digitalimage sensor used to capture the image.
 3. The method of claim 1 whereinthe group of pixels is a plurality of pixel rows of the image.
 4. Themethod of claim 3 wherein the low pass filtering produces a single rowof pixel values that contain portions of the objects.
 5. The method ofclaim 1 wherein analyzing the filtered group of pixels comprisescalculating a centroid of the first subgroup of pixels, and a centroidof the second subgroup of pixels, wherein the computed pixel distance isthe distance between the centroids.
 6. The method of claim 1 furthercomprising: signaling an alert when the converted true distance differsfrom an expected true distance.
 7. The method of claim 6 furthercomprising: repeating an imaging performance test of the cameracomponent in response to the alert.
 8. The method of claim 6 wherein theconverted true distance is an initial value computed just after thecamera component has been installed in a test fixture, the methodfurther comprising repeating a)-d) to compute the expected truedistance.
 9. A system for testing an optical characteristic of a cameracomponent, comprising: a test fixture in which the camera component isto be installed; a target which has a plurality of objects and islocated such that the camera component as installed is aimed at thetarget; and a test computer that signals an image sensor to capture animage of the objects in the target, low pass filters a group of pixelsof the image that represent portions of the objects, analyzes thefiltered group of pixels to compute a pixel distance between a firstsubgroup of pixels that represents a portion of one of the objects, anda second subgroup of pixels that represents a portion of another of theobjects, and converts the pixel distance into a true distance using apredetermined math relationship that relates a pixel distance variablewith a true distance variable.
 10. The system of claim 9 wherein thetest fixture is to receive a camera component that has an imaging lensinstalled in a lens holder.
 11. The system of claim 10 wherein the testfixture is to receive a camera module that has said image sensorintegrated with the lens holder in a camera module.
 12. The system ofclaim 10 wherein the test computer is to signal an alert when theconverted true distance differs from an expected true distance.
 13. Thesystem of claim 10 wherein the true distance variable refers to distancebetween the imaging lens and the image sensor.
 14. An article ofmanufacture comprising: a non-transitory machine-readable medium havingstored therein instructions that when executed by a processor signal acamera module under test to capture an image of a target, low passfilter a group of pixels of the image that represent portions of aplurality of objects in the target, analyze the filtered group of pixelsto compute a pixel distance between a first subgroup of pixels thatrepresents a portion of one of the objects, and a second subgroup ofpixels that represents a portion of another one of the objects, andconvert the pixel distance into a true distance using a predeterminedmath relationship that relates a pixel distance variable with a truedistance variable.
 15. The article of manufacture of claim 14 whereinthe instructions program the processor to signal an alert when theconverted true distance differs from an expected true distance.
 16. Thearticle of manufacture of claim 15 wherein the true distance variablerefers to distance between an imaging lens in the camera module undertest and an image sensor in the camera module.
 17. The article ofmanufacture of claim 14 wherein the instructions program the processorto analyze the filtered group of pixels by calculating a centroid of thefirst subgroup of pixels, and a centroid of the second subgroup ofpixels, and wherein the computed pixel distance is the distance betweenthe centroids.